Learning to decompose multimode fibers using a physics-informed neural network

Qian Zhang; Yuan Sui; Stefan Rothe; Jürgen W. Czarske

doi:10.1117/12.3027588

4 October 2024 Learning to decompose multimode fibers using a physics-informed neural network

Qian Zhang, Yuan Sui, Stefan Rothe, Jürgen W. Czarske

Author Affiliations +

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC1311815 (2024) https://doi.org/10.1117/12.3027588
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

Mode decomposition is a quantitative technique for analyzing multimode fibers. With pre-knowledge of the eigenmodes, the phase and amplitude weights of each mode can be extracted from the optical field. In this paper, we introduce a simple deep learning-based mode decomposition method by integrating a physical model with a deep neural network. We demonstrate that this method can decompose up to thousands of modes based on pure-intensity images.

Conference Presentation

Citation Download Citation

Qian Zhang, Yuan Sui, Stefan Rothe, and Jürgen W. Czarske "Learning to decompose multimode fibers using a physics-informed neural network", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC1311815 (4 October 2024); https://doi.org/10.1117/12.3027588

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